Learner-centered software engineering education: from resources to skills and pedagogical patterns
Bibliographic record
Abstract
A revolution is taking place in academic and continuing education, one that deals with the philosophy of how we teach and learn, the relationship between educators and learners, the way in which the classroom is structured, and the nature of the curriculum. This new approach, termed learner-centered education, is focused on the needs, skills and interests of the learner rather than on the organization of curriculum content. This paper describes an approach for identifying critical skills and for designing training material for learner-centered software engineering education. The approach starts from an analysis of the software developer's context of work, identifies critical skills and then associates relevant learning resources with them. The approach has been successfully used and validated in a real world-training program called PRISE that the first author developed-Programme de Reorientation des Ingenieurs Sans Emploi, a Curriculum for Retraining Unemployed Engineers in Software Engineering. The approach is also being used in some courses in the Concordia bachelor of software engineering program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".